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Field
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the spatial reasoning capabilities of multi-mode large language models and hybrid AI systems combining artificial neural networks with symbolic AI. In-house research and development of one of these systems
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the spatial reasoning capabilities of multi-mode large language models and hybrid AI systems combining artificial neural networks with symbolic AI. In-house research and development of one of these systems
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have: experience with spiking neural networks and neuromorphic architectures; deep knowledge about our brain and how it makes decisions; coding reinforcement learning algorithms/feedback systems and
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. At present there is specific interest in advanced 3D perception techniques such as geometric foundation models, implicit neural rendering (NeRF, Gaussian Splatting) as well as semantic mapping. Our research
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: Developing state-of-the-art neural networks to accelerate the development of formulations in the pharmaceutical sector. Collaborating on the implementation of these techniques within a company. Contributing to
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among the following ones, as well as collaboration in the teaching and third mission activities of the Faculty: 1) Gravitational wave research with the LIGO/Virgo/Kagra network and Einstein Telescope; 2
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: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
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neural circuit analysis with experience in: In vivo two-photon imaging or Electrophysiology in behaving animals Quantitative data analysis and computational modeling of network activity This is a full-time
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Description Applications are invited for a postdoctoral position to study the neurocomputational mechanisms of information propagation in social networks. The Neuroeconomics group of Dr Jean-Claude Dreher
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background and expertise in one or more of the following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural